An ensemble clusterer of multiple fuzzy k-means clusterings to recognize arbitrarily shaped clusters

Abstract

Fuzzy cluster ensemble is an important research component of ensemble learning, which is used to aggregate several fuzzy base clusterings to generate a single output clustering with improved robustness and quality. However, since clustering is unsupervised, where “accuracy” does not have a clear meaning, it is difficult for existing ensemble methods to integrate multiple fuzzy $k$ -means clusterings to find arbitrarily shaped clusters. To overcome the deficiency, we propose a new ensemble clusterer (algorithm) of multiple fuzzy $k$ -means clusterings based on a local hypothesis. In the new algorithm, we study the extraction of local-credible memberships from a base clustering, the production of multiple base clusterings with different local-credible spaces, and the construction of cluster relation based on indirect overlap of local-credible spaces. The proposed ensemble clusterer not only inherits the scalability of fuzzy $k$ -means but also overcomes the inability to find arbitrarily shaped clusters. We compare the proposed algorithm with other cluster ensemble algorithms on several synthetical and real datasets. The experimental results illustrate the effectiveness and efficiency of the proposed algorithm.